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Model: micymike/codemate-qwen-1.5B Source: Original Platform
2026-07-22 01:08:10 +08:00
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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-1.5B-instruct  # Change this to your exact Qwen base model repo
+tags:
+- text-generation
+- fine-tuned
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+
+# CodeMate-Qwen
+
+## Model Details
+
+### Model Description
+
+CodeMate-Qwen is a coding-focused language model fine-tuned from Qwen2.5-Coder-1.5B using Low-Rank Adaptation (LoRA). The model is designed to assist developers with code generation, debugging, code explanation, refactoring, and software engineering tasks.
+
+The project was created to explore parameter-efficient fine-tuning techniques and build a lightweight coding assistant capable of supporting real-world development workflows.
+
+### Developed by
+
+Michael Moses
+
+### Funded by
+
+Self-funded personal research project.
+
+### Shared by
+
+Michael Moses
+
+### Model Type
+
+Causal Language Model (LLM) for Code Generation and Software Engineering Assistance.
+
+### Language(s)
+
+* English
+* Programming Languages:
+
+  * Python
+  * JavaScript
+  * TypeScript
+  * HTML
+  * CSS
+  * SQL
+  * General programming concepts
+
+### License
+
+Apache 2.0 (subject to the licensing terms of the base Qwen model).
+
+### Finetuned From
+
+Qwen/Qwen2.5-Coder-1.5B
+
+---
+
+## Model Sources
+
+### Repository
+
+GitHub: https://github.com/micymike
+
+### Hugging Face
+
+https://huggingface.co/micymike
+
+### Demo
+
+Coming Soon
+
+---
+
+# Uses
+
+## Direct Use
+
+This model is intended for:
+
+* Code generation
+* Debugging assistance
+* Programming education
+* Code explanation
+* Refactoring recommendations
+* Developer productivity workflows
+* AI-assisted software development
+
+## Downstream Use
+
+Potential downstream applications include:
+
+* Coding copilots
+* Educational coding assistants
+* Automated code review systems
+* Software engineering support tools
+* Programming tutors
+
+## Out-of-Scope Use
+
+This model is not intended for:
+
+* Legal advice
+* Medical advice
+* Financial decision-making
+* Safety-critical systems
+* Autonomous code deployment without human review
+
+Generated code should always be reviewed and tested before production use.
+
+---
+
+# Bias, Risks, and Limitations
+
+Like all large language models, CodeMate-Qwen may:
+
+* Generate incorrect code
+* Produce insecure implementations
+* Hallucinate APIs or libraries
+* Miss edge cases
+* Reflect biases present in training data
+
+Users should validate all generated outputs before deployment.
+
+---
+
+# Recommendations
+
+The model performs best when:
+
+* Prompts are clear and specific
+* Sufficient context is provided
+* Outputs are reviewed by a developer
+
+The model should be considered an assistant rather than a replacement for software engineering expertise.
+
+---
+
+# How to Get Started
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "micymike/codemate-qwen-merged"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto"
+)
+
+prompt = "Write a Python function that checks if a number is prime."
+
+inputs = tokenizer(prompt, return_tensors="pt")
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+---
+
+# Training Details
+
+## Training Data
+
+The training dataset consisted of instruction-response pairs focused on software engineering and programming-related tasks.
+
+Examples included:
+
+* Bug fixing
+* Code generation
+* Code explanation
+* Refactoring
+* Programming Q&A
+* Developer workflow assistance
+
+## Training Procedure
+
+The model was fine-tuned using LoRA (Low-Rank Adaptation), allowing efficient adaptation of the base model while training only a small subset of parameters.
+
+### Training Regime
+
+* Base Model: Qwen2.5-Coder-1.5B
+* Fine-Tuning Method: LoRA
+* Framework: Hugging Face Transformers
+* PEFT Library: PEFT
+* Backend: PyTorch
+
+---
+
+# Evaluation
+
+## Testing Data
+
+Evaluation was performed using programming-related prompts covering:
+
+* Python debugging
+* Code generation
+* Code explanation
+* Refactoring tasks
+
+## Metrics
+
+Evaluation focused primarily on qualitative assessment:
+
+* Instruction-following capability
+* Code correctness
+* Response quality
+* Programming relevance
+
+## Results
+
+The model demonstrated improved performance on coding-focused tasks compared to the untuned base model and showed stronger alignment with software engineering workflows.
+
+---
+
+# Environmental Impact
+
+### Hardware Type
+
+NVIDIA GPU
+
+### Cloud Provider
+
+Google Colab
+
+### Compute Region
+
+Not specified
+
+### Carbon Emitted
+
+Not measured
+
+---
+
+# Technical Specifications
+
+## Model Architecture
+
+Transformer-based autoregressive language model.
+
+### Base Architecture
+
+Qwen2.5-Coder-1.5B
+
+### Objective
+
+Next-token prediction optimized for coding and software engineering tasks.
+
+---
+
+# Compute Infrastructure
+
+## Hardware
+
+Google Colab GPU Environment
+
+## Software
+
+* Python
+* PyTorch
+* Transformers
+* PEFT
+* Hugging Face Hub
+
+---
+
+# Citation
+
+```bibtex
+@misc{moses2026codemateqwen,
+  author = {Michael Moses},
+  title = {CodeMate-Qwen: A LoRA Fine-Tuned Coding Assistant Based on Qwen2.5-Coder-1.5B},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/micymike}
+}
+```
+
+---
+
+# Model Card Authors
+
+Michael Moses
+
+---
+
+# Contact
+
+GitHub: https://github.com/micymike
+
+Email: [mosesmichael878@gmail.com](mailto:mosesmichael878@gmail.com)
+
+---
+
+# Future Work
+
+Planned improvements include:
+
+* Larger instruction datasets
+* Quantized deployments
+* Benchmark evaluation on HumanEval and MBPP
+* Additional programming language support
+* Interactive web demo
+* Advanced code review capabilities